#Set working directory to appropriate folder for inputs and outputs on Google Drive
#Initialize
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')
#Plot cell cycle
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')
#load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/all_data_markers.RData')
cis_markers <- FindAllMarkers(cis, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 1
| | 0 % ~calculating
|++ | 3 % ~00s
|++++ | 7 % ~00s
|++++++ | 10% ~00s
|+++++++ | 14% ~00s
|+++++++++ | 17% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++ | 24% ~00s
|++++++++++++++ | 28% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++ | 34% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++ | 41% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~04s
|++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 3 % ~03s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 5 % ~06s
|+++ | 6 % ~06s
|++++ | 7 % ~06s
|+++++ | 8 % ~06s
|+++++ | 9 % ~06s
|++++++ | 10% ~06s
|++++++ | 12% ~06s
|+++++++ | 13% ~06s
|+++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|++++++++++ | 19% ~05s
|++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~05s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 33% ~04s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|+++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s
Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~04s
|+++++++ | 13% ~04s
|+++++++ | 14% ~04s
|++++++++ | 15% ~04s
|++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|++++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|+++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
cocl2_markers <- FindAllMarkers(cocl2, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 3 % ~01m 03s
|++ | 4 % ~42s
|+++ | 5 % ~32s
|++++ | 7 % ~25s
|++++ | 8 % ~21s
|+++++ | 9 % ~18s
|++++++ | 11% ~16s
|++++++ | 12% ~14s
|+++++++ | 13% ~13s
|++++++++ | 14% ~11s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|++++++++++ | 18% ~09s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~07s
|++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 7 % ~04s
|++++ | 8 % ~04s
|+++++ | 9 % ~04s
|+++++ | 10% ~04s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~02m 19s
|+ | 2 % ~01m 10s
|++ | 3 % ~47s
|++ | 4 % ~36s
|+++ | 5 % ~29s
|+++ | 6 % ~24s
|++++ | 7 % ~21s
|++++ | 8 % ~18s
|+++++ | 9 % ~16s
|+++++ | 10% ~15s
|++++++ | 11% ~13s
|++++++ | 12% ~12s
|+++++++ | 13% ~11s
|+++++++ | 14% ~11s
|++++++++ | 15% ~10s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|+++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|++++++++++ | 20% ~08s
|+++++++++++ | 21% ~07s
|+++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|+++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|+++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 3 % ~08s
|++ | 4 % ~06s
|+++ | 5 % ~05s
|++++ | 6 % ~04s
|++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~11s
|+ | 2 % ~11s
|++ | 3 % ~11s
|++ | 4 % ~11s
|+++ | 5 % ~11s
|+++ | 6 % ~11s
|++++ | 7 % ~11s
|++++ | 8 % ~11s
|+++++ | 9 % ~11s
|+++++ | 10% ~11s
|++++++ | 11% ~10s
|++++++ | 12% ~10s
|+++++++ | 13% ~10s
|+++++++ | 14% ~10s
|++++++++ | 15% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~10s
|++++++++++ | 20% ~09s
|+++++++++++ | 21% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~10s
|++++++++++++ | 24% ~10s
|+++++++++++++ | 25% ~10s
|+++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~09s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~08s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|+++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~08s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|+++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~08s
|+++ | 5 % ~08s
|++++ | 6 % ~08s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 16% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 18% ~07s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 30% ~06s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~05s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
dabtram_markers <- FindAllMarkers(dabtram, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 4 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~06s
|++++ | 7 % ~06s
|+++++ | 8 % ~06s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 13% ~05s
|++++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|++++++++++ | 18% ~05s
|++++++++++ | 19% ~05s
|+++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~05s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|+++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~10s
|++ | 3 % ~09s
|+++ | 4 % ~09s
|+++ | 5 % ~09s
|++++ | 6 % ~09s
|++++ | 7 % ~09s
|+++++ | 8 % ~09s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~09s
|+++++++ | 12% ~09s
|+++++++ | 13% ~08s
|++++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~08s
|+++++++++++ | 21% ~08s
|++++++++++++ | 22% ~08s
|++++++++++++ | 23% ~08s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|++++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|+++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~07s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~06s
|++++++++++++++++++ | 35% ~06s
|+++++++++++++++++++ | 36% ~06s
|+++++++++++++++++++ | 37% ~06s
|++++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|+++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|++++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|+++++++++++++++++++++++ | 44% ~05s
|+++++++++++++++++++++++ | 45% ~05s
|++++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s
all_data.markers <- FindAllMarkers(all_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~33s
|++ | 2 % ~33s
|++ | 3 % ~32s
|+++ | 4 % ~31s
|+++ | 5 % ~31s
|++++ | 6 % ~30s
|++++ | 7 % ~29s
|+++++ | 9 % ~28s
|+++++ | 10% ~28s
|++++++ | 11% ~28s
|++++++ | 12% ~27s
|+++++++ | 13% ~27s
|+++++++ | 14% ~27s
|++++++++ | 15% ~27s
|++++++++ | 16% ~26s
|+++++++++ | 17% ~26s
|++++++++++ | 18% ~26s
|++++++++++ | 19% ~26s
|+++++++++++ | 20% ~25s
|+++++++++++ | 21% ~28s
|++++++++++++ | 22% ~27s
|++++++++++++ | 23% ~27s
|+++++++++++++ | 24% ~26s
|+++++++++++++ | 26% ~26s
|++++++++++++++ | 27% ~26s
|++++++++++++++ | 28% ~25s
|+++++++++++++++ | 29% ~25s
|+++++++++++++++ | 30% ~24s
|++++++++++++++++ | 31% ~24s
|++++++++++++++++ | 32% ~23s
|+++++++++++++++++ | 33% ~23s
|++++++++++++++++++ | 34% ~22s
|++++++++++++++++++ | 35% ~22s
|+++++++++++++++++++ | 36% ~22s
|+++++++++++++++++++ | 37% ~21s
|++++++++++++++++++++ | 38% ~21s
|++++++++++++++++++++ | 39% ~20s
|+++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 41% ~20s
|++++++++++++++++++++++ | 43% ~19s
|++++++++++++++++++++++ | 44% ~19s
|+++++++++++++++++++++++ | 45% ~19s
|+++++++++++++++++++++++ | 46% ~18s
|++++++++++++++++++++++++ | 47% ~18s
|++++++++++++++++++++++++ | 48% ~18s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~17s
|++++++++++++++++++++++++++ | 51% ~16s
|+++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~16s
|++++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 56% ~15s
|+++++++++++++++++++++++++++++ | 57% ~14s
|++++++++++++++++++++++++++++++ | 59% ~14s
|++++++++++++++++++++++++++++++ | 60% ~14s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|+++++++++++++++++++++++++++++++ | 62% ~13s
|++++++++++++++++++++++++++++++++ | 63% ~13s
|++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~12s
|+++++++++++++++++++++++++++++++++ | 66% ~12s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~11s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|++++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|++++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=34s
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~31s
|++ | 2 % ~29s
|++ | 3 % ~30s
|+++ | 4 % ~29s
|+++ | 5 % ~29s
|++++ | 7 % ~28s
|++++ | 8 % ~30s
|+++++ | 9 % ~29s
|+++++ | 10% ~29s
|++++++ | 11% ~28s
|+++++++ | 12% ~27s
|+++++++ | 13% ~27s
|++++++++ | 14% ~26s
|++++++++ | 15% ~26s
|+++++++++ | 16% ~26s
|+++++++++ | 18% ~25s
|++++++++++ | 19% ~25s
|++++++++++ | 20% ~24s
|+++++++++++ | 21% ~24s
|+++++++++++ | 22% ~24s
|++++++++++++ | 23% ~23s
|+++++++++++++ | 24% ~23s
|+++++++++++++ | 25% ~22s
|++++++++++++++ | 26% ~22s
|++++++++++++++ | 27% ~22s
|+++++++++++++++ | 29% ~21s
|+++++++++++++++ | 30% ~21s
|++++++++++++++++ | 31% ~21s
|++++++++++++++++ | 32% ~20s
|+++++++++++++++++ | 33% ~20s
|++++++++++++++++++ | 34% ~20s
|++++++++++++++++++ | 35% ~19s
|+++++++++++++++++++ | 36% ~19s
|+++++++++++++++++++ | 37% ~19s
|++++++++++++++++++++ | 38% ~18s
|++++++++++++++++++++ | 40% ~18s
|+++++++++++++++++++++ | 41% ~18s
|+++++++++++++++++++++ | 42% ~17s
|++++++++++++++++++++++ | 43% ~17s
|++++++++++++++++++++++ | 44% ~17s
|+++++++++++++++++++++++ | 45% ~17s
|++++++++++++++++++++++++ | 46% ~16s
|++++++++++++++++++++++++ | 47% ~16s
|+++++++++++++++++++++++++ | 48% ~16s
|+++++++++++++++++++++++++ | 49% ~15s
|++++++++++++++++++++++++++ | 51% ~15s
|++++++++++++++++++++++++++ | 52% ~15s
|+++++++++++++++++++++++++++ | 53% ~14s
|+++++++++++++++++++++++++++ | 54% ~14s
|++++++++++++++++++++++++++++ | 55% ~14s
|+++++++++++++++++++++++++++++ | 56% ~13s
|+++++++++++++++++++++++++++++ | 57% ~13s
|++++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 59% ~12s
|+++++++++++++++++++++++++++++++ | 60% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~11s
|++++++++++++++++++++++++++++++++ | 64% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~10s
|++++++++++++++++++++++++++++++++++ | 67% ~10s
|+++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~09s
|++++++++++++++++++++++++++++++++++++ | 70% ~09s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~08s
|+++++++++++++++++++++++++++++++++++++ | 74% ~08s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|++++++++++++++++++++++++++++++++++++++ | 76% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01m 10s
|++ | 2 % ~01m 10s
|++ | 3 % ~01m 08s
|+++ | 5 % ~01m 08s
|+++ | 6 % ~01m 07s
|++++ | 7 % ~01m 06s
|+++++ | 8 % ~01m 05s
|+++++ | 9 % ~01m 04s
|++++++ | 10% ~01m 03s
|++++++ | 11% ~01m 02s
|+++++++ | 13% ~01m 02s
|+++++++ | 14% ~01m 01s
|++++++++ | 15% ~01m 01s
|+++++++++ | 16% ~01m 00s
|+++++++++ | 17% ~60s
|++++++++++ | 18% ~59s
|++++++++++ | 20% ~58s
|+++++++++++ | 21% ~57s
|+++++++++++ | 22% ~56s
|++++++++++++ | 23% ~56s
|+++++++++++++ | 24% ~55s
|+++++++++++++ | 25% ~54s
|++++++++++++++ | 26% ~54s
|++++++++++++++ | 28% ~53s
|+++++++++++++++ | 29% ~52s
|+++++++++++++++ | 30% ~51s
|++++++++++++++++ | 31% ~50s
|+++++++++++++++++ | 32% ~49s
|+++++++++++++++++ | 33% ~49s
|++++++++++++++++++ | 34% ~48s
|++++++++++++++++++ | 36% ~47s
|+++++++++++++++++++ | 37% ~46s
|+++++++++++++++++++ | 38% ~45s
|++++++++++++++++++++ | 39% ~45s
|+++++++++++++++++++++ | 40% ~44s
|+++++++++++++++++++++ | 41% ~43s
|++++++++++++++++++++++ | 43% ~43s
|++++++++++++++++++++++ | 44% ~42s
|+++++++++++++++++++++++ | 45% ~41s
|+++++++++++++++++++++++ | 46% ~40s
|++++++++++++++++++++++++ | 47% ~39s
|+++++++++++++++++++++++++ | 48% ~38s
|+++++++++++++++++++++++++ | 49% ~37s
|++++++++++++++++++++++++++ | 51% ~36s
|++++++++++++++++++++++++++ | 52% ~35s
|+++++++++++++++++++++++++++ | 53% ~35s
|++++++++++++++++++++++++++++ | 54% ~34s
|++++++++++++++++++++++++++++ | 55% ~33s
|+++++++++++++++++++++++++++++ | 56% ~32s
|+++++++++++++++++++++++++++++ | 57% ~31s
|++++++++++++++++++++++++++++++ | 59% ~30s
|++++++++++++++++++++++++++++++ | 60% ~29s
|+++++++++++++++++++++++++++++++ | 61% ~29s
|++++++++++++++++++++++++++++++++ | 62% ~28s
|++++++++++++++++++++++++++++++++ | 63% ~27s
|+++++++++++++++++++++++++++++++++ | 64% ~26s
|+++++++++++++++++++++++++++++++++ | 66% ~25s
|++++++++++++++++++++++++++++++++++ | 67% ~25s
|++++++++++++++++++++++++++++++++++ | 68% ~24s
|+++++++++++++++++++++++++++++++++++ | 69% ~23s
|++++++++++++++++++++++++++++++++++++ | 70% ~22s
|++++++++++++++++++++++++++++++++++++ | 71% ~21s
|+++++++++++++++++++++++++++++++++++++ | 72% ~20s
|+++++++++++++++++++++++++++++++++++++ | 74% ~19s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 14s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~01m 09s
|++ | 2 % ~01m 07s
|++ | 3 % ~01m 07s
|+++ | 4 % ~01m 05s
|+++ | 5 % ~01m 05s
|++++ | 6 % ~01m 03s
|++++ | 7 % ~01m 02s
|+++++ | 8 % ~01m 01s
|+++++ | 9 % ~01m 00s
|++++++ | 10% ~01m 06s
|++++++ | 11% ~01m 05s
|+++++++ | 12% ~01m 03s
|+++++++ | 14% ~01m 02s
|++++++++ | 15% ~01m 01s
|++++++++ | 16% ~60s
|+++++++++ | 17% ~59s
|+++++++++ | 18% ~58s
|++++++++++ | 19% ~57s
|++++++++++ | 20% ~56s
|+++++++++++ | 21% ~55s
|+++++++++++ | 22% ~55s
|++++++++++++ | 23% ~54s
|++++++++++++ | 24% ~53s
|+++++++++++++ | 25% ~52s
|++++++++++++++ | 26% ~51s
|++++++++++++++ | 27% ~51s
|+++++++++++++++ | 28% ~50s
|+++++++++++++++ | 29% ~49s
|++++++++++++++++ | 30% ~48s
|++++++++++++++++ | 31% ~48s
|+++++++++++++++++ | 32% ~47s
|+++++++++++++++++ | 33% ~46s
|++++++++++++++++++ | 34% ~46s
|++++++++++++++++++ | 35% ~45s
|+++++++++++++++++++ | 36% ~44s
|+++++++++++++++++++ | 38% ~43s
|++++++++++++++++++++ | 39% ~43s
|++++++++++++++++++++ | 40% ~42s
|+++++++++++++++++++++ | 41% ~41s
|+++++++++++++++++++++ | 42% ~41s
|++++++++++++++++++++++ | 43% ~40s
|++++++++++++++++++++++ | 44% ~39s
|+++++++++++++++++++++++ | 45% ~38s
|+++++++++++++++++++++++ | 46% ~38s
|++++++++++++++++++++++++ | 47% ~37s
|++++++++++++++++++++++++ | 48% ~36s
|+++++++++++++++++++++++++ | 49% ~35s
|+++++++++++++++++++++++++ | 50% ~35s
|++++++++++++++++++++++++++ | 51% ~34s
|+++++++++++++++++++++++++++ | 52% ~33s
|+++++++++++++++++++++++++++ | 53% ~33s
|++++++++++++++++++++++++++++ | 54% ~32s
|++++++++++++++++++++++++++++ | 55% ~31s
|+++++++++++++++++++++++++++++ | 56% ~31s
|+++++++++++++++++++++++++++++ | 57% ~30s
|++++++++++++++++++++++++++++++ | 58% ~29s
|++++++++++++++++++++++++++++++ | 59% ~28s
|+++++++++++++++++++++++++++++++ | 60% ~28s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|++++++++++++++++++++++++++++++++ | 62% ~26s
|++++++++++++++++++++++++++++++++ | 64% ~25s
|+++++++++++++++++++++++++++++++++ | 65% ~25s
|+++++++++++++++++++++++++++++++++ | 66% ~24s
|++++++++++++++++++++++++++++++++++ | 67% ~23s
|++++++++++++++++++++++++++++++++++ | 68% ~22s
|+++++++++++++++++++++++++++++++++++ | 69% ~22s
|+++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 71% ~20s
|++++++++++++++++++++++++++++++++++++ | 72% ~19s
|+++++++++++++++++++++++++++++++++++++ | 73% ~19s
|+++++++++++++++++++++++++++++++++++++ | 74% ~18s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 09s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~01m 45s
|++ | 2 % ~01m 43s
|++ | 3 % ~01m 41s
|+++ | 4 % ~01m 40s
|+++ | 5 % ~01m 37s
|++++ | 6 % ~01m 34s
|++++ | 7 % ~01m 33s
|+++++ | 8 % ~01m 32s
|+++++ | 9 % ~01m 31s
|++++++ | 10% ~01m 30s
|++++++ | 11% ~01m 30s
|+++++++ | 12% ~01m 29s
|+++++++ | 13% ~01m 28s
|++++++++ | 14% ~01m 27s
|++++++++ | 15% ~01m 27s
|+++++++++ | 16% ~01m 26s
|+++++++++ | 17% ~01m 25s
|++++++++++ | 18% ~01m 24s
|++++++++++ | 19% ~01m 23s
|+++++++++++ | 20% ~01m 22s
|+++++++++++ | 21% ~01m 21s
|++++++++++++ | 22% ~01m 20s
|++++++++++++ | 23% ~01m 20s
|+++++++++++++ | 24% ~01m 18s
|+++++++++++++ | 26% ~01m 17s
|++++++++++++++ | 27% ~01m 16s
|++++++++++++++ | 28% ~01m 15s
|+++++++++++++++ | 29% ~01m 14s
|+++++++++++++++ | 30% ~01m 14s
|++++++++++++++++ | 31% ~01m 13s
|++++++++++++++++ | 32% ~01m 12s
|+++++++++++++++++ | 33% ~01m 10s
|+++++++++++++++++ | 34% ~01m 09s
|++++++++++++++++++ | 35% ~01m 08s
|++++++++++++++++++ | 36% ~01m 07s
|+++++++++++++++++++ | 37% ~01m 06s
|+++++++++++++++++++ | 38% ~01m 05s
|++++++++++++++++++++ | 39% ~01m 03s
|++++++++++++++++++++ | 40% ~01m 02s
|+++++++++++++++++++++ | 41% ~01m 01s
|+++++++++++++++++++++ | 42% ~01m 00s
|++++++++++++++++++++++ | 43% ~59s
|++++++++++++++++++++++ | 44% ~58s
|+++++++++++++++++++++++ | 45% ~57s
|+++++++++++++++++++++++ | 46% ~56s
|++++++++++++++++++++++++ | 47% ~55s
|++++++++++++++++++++++++ | 48% ~54s
|+++++++++++++++++++++++++ | 49% ~53s
|+++++++++++++++++++++++++ | 50% ~52s
|++++++++++++++++++++++++++ | 51% ~51s
|+++++++++++++++++++++++++++ | 52% ~50s
|+++++++++++++++++++++++++++ | 53% ~49s
|++++++++++++++++++++++++++++ | 54% ~48s
|++++++++++++++++++++++++++++ | 55% ~47s
|+++++++++++++++++++++++++++++ | 56% ~46s
|+++++++++++++++++++++++++++++ | 57% ~45s
|++++++++++++++++++++++++++++++ | 58% ~44s
|++++++++++++++++++++++++++++++ | 59% ~43s
|+++++++++++++++++++++++++++++++ | 60% ~42s
|+++++++++++++++++++++++++++++++ | 61% ~41s
|++++++++++++++++++++++++++++++++ | 62% ~40s
|++++++++++++++++++++++++++++++++ | 63% ~39s
|+++++++++++++++++++++++++++++++++ | 64% ~38s
|+++++++++++++++++++++++++++++++++ | 65% ~37s
|++++++++++++++++++++++++++++++++++ | 66% ~35s
|++++++++++++++++++++++++++++++++++ | 67% ~34s
|+++++++++++++++++++++++++++++++++++ | 68% ~33s
|+++++++++++++++++++++++++++++++++++ | 69% ~32s
|++++++++++++++++++++++++++++++++++++ | 70% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|+++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|++++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 45s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~30s
|++ | 3 % ~30s
|++ | 4 % ~29s
|+++ | 5 % ~29s
|++++ | 7 % ~28s
|++++ | 8 % ~28s
|+++++ | 9 % ~28s
|++++++ | 11% ~27s
|++++++ | 12% ~27s
|+++++++ | 13% ~27s
|++++++++ | 14% ~26s
|++++++++ | 16% ~26s
|+++++++++ | 17% ~25s
|++++++++++ | 18% ~25s
|++++++++++ | 20% ~25s
|+++++++++++ | 21% ~24s
|++++++++++++ | 22% ~24s
|++++++++++++ | 24% ~24s
|+++++++++++++ | 25% ~23s
|++++++++++++++ | 26% ~23s
|++++++++++++++ | 28% ~23s
|+++++++++++++++ | 29% ~22s
|++++++++++++++++ | 30% ~22s
|++++++++++++++++ | 32% ~21s
|+++++++++++++++++ | 33% ~21s
|++++++++++++++++++ | 34% ~21s
|++++++++++++++++++ | 36% ~20s
|+++++++++++++++++++ | 37% ~20s
|++++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 39% ~19s
|+++++++++++++++++++++ | 41% ~19s
|++++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|++++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 47% ~17s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|+++++++++++++++++++++++++++ | 53% ~16s
|+++++++++++++++++++++++++++ | 54% ~16s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 57% ~15s
|+++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~14s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|+++++++++++++++++++++++++++++++ | 62% ~13s
|++++++++++++++++++++++++++++++++ | 63% ~13s
|+++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 66% ~12s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~11s
|+++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|+++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=34s
Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~01m 04s
|++ | 2 % ~01m 02s
|++ | 4 % ~01m 01s
|+++ | 5 % ~01m 01s
|+++ | 6 % ~60s
|++++ | 7 % ~59s
|+++++ | 8 % ~58s
|+++++ | 10% ~01m 00s
|++++++ | 11% ~59s
|++++++ | 12% ~58s
|+++++++ | 13% ~57s
|++++++++ | 14% ~56s
|++++++++ | 15% ~55s
|+++++++++ | 17% ~54s
|+++++++++ | 18% ~53s
|++++++++++ | 19% ~52s
|+++++++++++ | 20% ~52s
|+++++++++++ | 21% ~51s
|++++++++++++ | 23% ~50s
|++++++++++++ | 24% ~50s
|+++++++++++++ | 25% ~49s
|++++++++++++++ | 26% ~48s
|++++++++++++++ | 27% ~47s
|+++++++++++++++ | 29% ~47s
|+++++++++++++++ | 30% ~46s
|++++++++++++++++ | 31% ~45s
|+++++++++++++++++ | 32% ~44s
|+++++++++++++++++ | 33% ~44s
|++++++++++++++++++ | 35% ~43s
|++++++++++++++++++ | 36% ~42s
|+++++++++++++++++++ | 37% ~41s
|++++++++++++++++++++ | 38% ~41s
|++++++++++++++++++++ | 39% ~40s
|+++++++++++++++++++++ | 40% ~39s
|+++++++++++++++++++++ | 42% ~38s
|++++++++++++++++++++++ | 43% ~38s
|+++++++++++++++++++++++ | 44% ~37s
|+++++++++++++++++++++++ | 45% ~36s
|++++++++++++++++++++++++ | 46% ~35s
|++++++++++++++++++++++++ | 48% ~35s
|+++++++++++++++++++++++++ | 49% ~34s
|+++++++++++++++++++++++++ | 50% ~33s
|++++++++++++++++++++++++++ | 51% ~33s
|+++++++++++++++++++++++++++ | 52% ~32s
|+++++++++++++++++++++++++++ | 54% ~31s
|++++++++++++++++++++++++++++ | 55% ~30s
|++++++++++++++++++++++++++++ | 56% ~29s
|+++++++++++++++++++++++++++++ | 57% ~29s
|++++++++++++++++++++++++++++++ | 58% ~28s
|++++++++++++++++++++++++++++++ | 60% ~27s
|+++++++++++++++++++++++++++++++ | 61% ~26s
|+++++++++++++++++++++++++++++++ | 62% ~25s
|++++++++++++++++++++++++++++++++ | 63% ~24s
|+++++++++++++++++++++++++++++++++ | 64% ~24s
|+++++++++++++++++++++++++++++++++ | 65% ~23s
|++++++++++++++++++++++++++++++++++ | 67% ~22s
|++++++++++++++++++++++++++++++++++ | 68% ~21s
|+++++++++++++++++++++++++++++++++++ | 69% ~20s
|++++++++++++++++++++++++++++++++++++ | 70% ~20s
|++++++++++++++++++++++++++++++++++++ | 71% ~19s
|+++++++++++++++++++++++++++++++++++++ | 73% ~18s
|+++++++++++++++++++++++++++++++++++++ | 74% ~17s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 08s
Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 3 % ~14s
|++ | 4 % ~14s
|+++ | 5 % ~14s
|++++ | 7 % ~13s
|++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 11% ~12s
|++++++ | 12% ~12s
|+++++++ | 13% ~12s
|++++++++ | 15% ~12s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|++++++++++ | 19% ~11s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~10s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~09s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 35% ~09s
|++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01m 38s
|++ | 2 % ~01m 39s
|++ | 3 % ~01m 37s
|+++ | 4 % ~01m 42s
|+++ | 5 % ~01m 38s
|++++ | 6 % ~01m 36s
|++++ | 7 % ~01m 35s
|+++++ | 8 % ~01m 34s
|+++++ | 9 % ~01m 32s
|++++++ | 10% ~01m 31s
|++++++ | 11% ~01m 31s
|+++++++ | 12% ~01m 30s
|+++++++ | 14% ~01m 29s
|++++++++ | 15% ~01m 29s
|++++++++ | 16% ~01m 27s
|+++++++++ | 17% ~01m 26s
|+++++++++ | 18% ~01m 25s
|++++++++++ | 19% ~01m 24s
|++++++++++ | 20% ~01m 23s
|+++++++++++ | 21% ~01m 22s
|+++++++++++ | 22% ~01m 21s
|++++++++++++ | 23% ~01m 20s
|++++++++++++ | 24% ~01m 19s
|+++++++++++++ | 25% ~01m 18s
|++++++++++++++ | 26% ~01m 17s
|++++++++++++++ | 27% ~01m 17s
|+++++++++++++++ | 28% ~01m 15s
|+++++++++++++++ | 29% ~01m 14s
|++++++++++++++++ | 30% ~01m 13s
|++++++++++++++++ | 31% ~01m 11s
|+++++++++++++++++ | 32% ~01m 10s
|+++++++++++++++++ | 33% ~01m 09s
|++++++++++++++++++ | 34% ~01m 07s
|++++++++++++++++++ | 35% ~01m 06s
|+++++++++++++++++++ | 36% ~01m 05s
|+++++++++++++++++++ | 38% ~01m 04s
|++++++++++++++++++++ | 39% ~01m 03s
|++++++++++++++++++++ | 40% ~01m 02s
|+++++++++++++++++++++ | 41% ~01m 01s
|+++++++++++++++++++++ | 42% ~01m 00s
|++++++++++++++++++++++ | 43% ~59s
|++++++++++++++++++++++ | 44% ~58s
|+++++++++++++++++++++++ | 45% ~57s
|+++++++++++++++++++++++ | 46% ~56s
|++++++++++++++++++++++++ | 47% ~55s
|++++++++++++++++++++++++ | 48% ~54s
|+++++++++++++++++++++++++ | 49% ~53s
|+++++++++++++++++++++++++ | 50% ~52s
|++++++++++++++++++++++++++ | 51% ~51s
|+++++++++++++++++++++++++++ | 52% ~50s
|+++++++++++++++++++++++++++ | 53% ~49s
|++++++++++++++++++++++++++++ | 54% ~47s
|++++++++++++++++++++++++++++ | 55% ~46s
|+++++++++++++++++++++++++++++ | 56% ~45s
|+++++++++++++++++++++++++++++ | 57% ~44s
|++++++++++++++++++++++++++++++ | 58% ~43s
|++++++++++++++++++++++++++++++ | 59% ~42s
|+++++++++++++++++++++++++++++++ | 60% ~41s
|+++++++++++++++++++++++++++++++ | 61% ~40s
|++++++++++++++++++++++++++++++++ | 62% ~39s
|++++++++++++++++++++++++++++++++ | 64% ~38s
|+++++++++++++++++++++++++++++++++ | 65% ~37s
|+++++++++++++++++++++++++++++++++ | 66% ~35s
|++++++++++++++++++++++++++++++++++ | 67% ~34s
|++++++++++++++++++++++++++++++++++ | 68% ~33s
|+++++++++++++++++++++++++++++++++++ | 69% ~32s
|+++++++++++++++++++++++++++++++++++ | 70% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|+++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 75% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 44s
Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~02m 18s
|++ | 2 % ~02m 18s
|++ | 3 % ~02m 14s
|+++ | 4 % ~02m 12s
|+++ | 5 % ~02m 11s
|++++ | 6 % ~02m 10s
|++++ | 7 % ~02m 09s
|+++++ | 9 % ~02m 09s
|+++++ | 10% ~02m 08s
|++++++ | 11% ~02m 07s
|++++++ | 12% ~02m 06s
|+++++++ | 13% ~02m 05s
|+++++++ | 14% ~02m 03s
|++++++++ | 15% ~02m 03s
|++++++++ | 16% ~02m 01s
|+++++++++ | 17% ~01m 59s
|++++++++++ | 18% ~01m 58s
|++++++++++ | 19% ~01m 56s
|+++++++++++ | 20% ~01m 54s
|+++++++++++ | 21% ~01m 52s
|++++++++++++ | 22% ~01m 51s
|++++++++++++ | 23% ~01m 49s
|+++++++++++++ | 24% ~01m 48s
|+++++++++++++ | 26% ~01m 46s
|++++++++++++++ | 27% ~01m 45s
|++++++++++++++ | 28% ~01m 43s
|+++++++++++++++ | 29% ~01m 42s
|+++++++++++++++ | 30% ~01m 40s
|++++++++++++++++ | 31% ~01m 39s
|++++++++++++++++ | 32% ~01m 38s
|+++++++++++++++++ | 33% ~01m 36s
|++++++++++++++++++ | 34% ~01m 34s
|++++++++++++++++++ | 35% ~01m 33s
|+++++++++++++++++++ | 36% ~01m 31s
|+++++++++++++++++++ | 37% ~01m 29s
|++++++++++++++++++++ | 38% ~01m 28s
|++++++++++++++++++++ | 39% ~01m 26s
|+++++++++++++++++++++ | 40% ~01m 25s
|+++++++++++++++++++++ | 41% ~01m 23s
|++++++++++++++++++++++ | 43% ~01m 22s
|++++++++++++++++++++++ | 44% ~01m 21s
|+++++++++++++++++++++++ | 45% ~01m 19s
|+++++++++++++++++++++++ | 46% ~01m 18s
|++++++++++++++++++++++++ | 47% ~01m 17s
|++++++++++++++++++++++++ | 48% ~01m 15s
|+++++++++++++++++++++++++ | 49% ~01m 13s
|+++++++++++++++++++++++++ | 50% ~01m 12s
|++++++++++++++++++++++++++ | 51% ~01m 10s
|+++++++++++++++++++++++++++ | 52% ~01m 08s
|+++++++++++++++++++++++++++ | 53% ~01m 07s
|++++++++++++++++++++++++++++ | 54% ~01m 05s
|++++++++++++++++++++++++++++ | 55% ~01m 04s
|+++++++++++++++++++++++++++++ | 56% ~01m 02s
|+++++++++++++++++++++++++++++ | 57% ~01m 01s
|++++++++++++++++++++++++++++++ | 59% ~59s
|++++++++++++++++++++++++++++++ | 60% ~58s
|+++++++++++++++++++++++++++++++ | 61% ~56s
|+++++++++++++++++++++++++++++++ | 62% ~55s
|++++++++++++++++++++++++++++++++ | 63% ~53s
|++++++++++++++++++++++++++++++++ | 64% ~52s
|+++++++++++++++++++++++++++++++++ | 65% ~50s
|+++++++++++++++++++++++++++++++++ | 66% ~49s
|++++++++++++++++++++++++++++++++++ | 67% ~47s
|+++++++++++++++++++++++++++++++++++ | 68% ~46s
|+++++++++++++++++++++++++++++++++++ | 69% ~44s
|++++++++++++++++++++++++++++++++++++ | 70% ~43s
|++++++++++++++++++++++++++++++++++++ | 71% ~41s
|+++++++++++++++++++++++++++++++++++++ | 72% ~39s
|+++++++++++++++++++++++++++++++++++++ | 73% ~38s
|++++++++++++++++++++++++++++++++++++++ | 74% ~36s
|++++++++++++++++++++++++++++++++++++++ | 76% ~35s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~33s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~32s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~30s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~29s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~27s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~26s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~23s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 22s
Calculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~45s
|++ | 2 % ~46s
|++ | 3 % ~45s
|+++ | 4 % ~45s
|+++ | 5 % ~44s
|++++ | 7 % ~43s
|++++ | 8 % ~43s
|+++++ | 9 % ~42s
|+++++ | 10% ~41s
|++++++ | 11% ~41s
|++++++ | 12% ~40s
|+++++++ | 13% ~40s
|++++++++ | 14% ~40s
|++++++++ | 15% ~39s
|+++++++++ | 16% ~39s
|+++++++++ | 17% ~39s
|++++++++++ | 18% ~38s
|++++++++++ | 20% ~38s
|+++++++++++ | 21% ~38s
|+++++++++++ | 22% ~37s
|++++++++++++ | 23% ~37s
|++++++++++++ | 24% ~36s
|+++++++++++++ | 25% ~36s
|++++++++++++++ | 26% ~36s
|++++++++++++++ | 27% ~35s
|+++++++++++++++ | 28% ~35s
|+++++++++++++++ | 29% ~34s
|++++++++++++++++ | 30% ~34s
|++++++++++++++++ | 32% ~33s
|+++++++++++++++++ | 33% ~33s
|+++++++++++++++++ | 34% ~32s
|++++++++++++++++++ | 35% ~32s
|++++++++++++++++++ | 36% ~31s
|+++++++++++++++++++ | 37% ~31s
|++++++++++++++++++++ | 38% ~31s
|++++++++++++++++++++ | 39% ~30s
|+++++++++++++++++++++ | 40% ~30s
|+++++++++++++++++++++ | 41% ~29s
|++++++++++++++++++++++ | 42% ~28s
|++++++++++++++++++++++ | 43% ~28s
|+++++++++++++++++++++++ | 45% ~27s
|+++++++++++++++++++++++ | 46% ~27s
|++++++++++++++++++++++++ | 47% ~26s
|++++++++++++++++++++++++ | 48% ~26s
|+++++++++++++++++++++++++ | 49% ~25s
|+++++++++++++++++++++++++ | 50% ~24s
|++++++++++++++++++++++++++ | 51% ~24s
|+++++++++++++++++++++++++++ | 52% ~23s
|+++++++++++++++++++++++++++ | 53% ~23s
|++++++++++++++++++++++++++++ | 54% ~22s
|++++++++++++++++++++++++++++ | 55% ~22s
|+++++++++++++++++++++++++++++ | 57% ~21s
|+++++++++++++++++++++++++++++ | 58% ~21s
|++++++++++++++++++++++++++++++ | 59% ~20s
|++++++++++++++++++++++++++++++ | 60% ~20s
|+++++++++++++++++++++++++++++++ | 61% ~19s
|+++++++++++++++++++++++++++++++ | 62% ~19s
|++++++++++++++++++++++++++++++++ | 63% ~18s
|+++++++++++++++++++++++++++++++++ | 64% ~18s
|+++++++++++++++++++++++++++++++++ | 65% ~17s
|++++++++++++++++++++++++++++++++++ | 66% ~16s
|++++++++++++++++++++++++++++++++++ | 67% ~16s
|+++++++++++++++++++++++++++++++++++ | 68% ~15s
|+++++++++++++++++++++++++++++++++++ | 70% ~15s
|++++++++++++++++++++++++++++++++++++ | 71% ~14s
|++++++++++++++++++++++++++++++++++++ | 72% ~14s
|+++++++++++++++++++++++++++++++++++++ | 73% ~13s
|+++++++++++++++++++++++++++++++++++++ | 74% ~13s
|++++++++++++++++++++++++++++++++++++++ | 75% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=50s
Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~02m 58s
|++ | 2 % ~02m 58s
|++ | 3 % ~03m 01s
|+++ | 4 % ~02m 55s
|+++ | 5 % ~02m 50s
|++++ | 6 % ~02m 48s
|++++ | 7 % ~02m 46s
|+++++ | 8 % ~02m 45s
|+++++ | 9 % ~02m 43s
|++++++ | 11% ~02m 42s
|++++++ | 12% ~02m 40s
|+++++++ | 13% ~02m 39s
|+++++++ | 14% ~02m 37s
|++++++++ | 15% ~02m 36s
|++++++++ | 16% ~02m 34s
|+++++++++ | 17% ~02m 34s
|+++++++++ | 18% ~02m 32s
|++++++++++ | 19% ~02m 29s
|++++++++++ | 20% ~02m 27s
|+++++++++++ | 21% ~02m 24s
|++++++++++++ | 22% ~02m 22s
|++++++++++++ | 23% ~02m 20s
|+++++++++++++ | 24% ~02m 18s
|+++++++++++++ | 25% ~02m 16s
|++++++++++++++ | 26% ~02m 14s
|++++++++++++++ | 27% ~02m 12s
|+++++++++++++++ | 28% ~02m 10s
|+++++++++++++++ | 29% ~02m 09s
|++++++++++++++++ | 31% ~02m 07s
|++++++++++++++++ | 32% ~02m 04s
|+++++++++++++++++ | 33% ~02m 02s
|+++++++++++++++++ | 34% ~02m 00s
|++++++++++++++++++ | 35% ~01m 58s
|++++++++++++++++++ | 36% ~01m 56s
|+++++++++++++++++++ | 37% ~01m 54s
|+++++++++++++++++++ | 38% ~01m 52s
|++++++++++++++++++++ | 39% ~01m 50s
|++++++++++++++++++++ | 40% ~01m 49s
|+++++++++++++++++++++ | 41% ~01m 47s
|++++++++++++++++++++++ | 42% ~01m 45s
|++++++++++++++++++++++ | 43% ~01m 44s
|+++++++++++++++++++++++ | 44% ~01m 41s
|+++++++++++++++++++++++ | 45% ~01m 39s
|++++++++++++++++++++++++ | 46% ~01m 37s
|++++++++++++++++++++++++ | 47% ~01m 35s
|+++++++++++++++++++++++++ | 48% ~01m 33s
|+++++++++++++++++++++++++ | 49% ~01m 31s
|++++++++++++++++++++++++++ | 51% ~01m 29s
|++++++++++++++++++++++++++ | 52% ~01m 27s
|+++++++++++++++++++++++++++ | 53% ~01m 26s
|+++++++++++++++++++++++++++ | 54% ~01m 24s
|++++++++++++++++++++++++++++ | 55% ~01m 22s
|++++++++++++++++++++++++++++ | 56% ~01m 20s
|+++++++++++++++++++++++++++++ | 57% ~01m 18s
|+++++++++++++++++++++++++++++ | 58% ~01m 16s
|++++++++++++++++++++++++++++++ | 59% ~01m 14s
|++++++++++++++++++++++++++++++ | 60% ~01m 12s
|+++++++++++++++++++++++++++++++ | 61% ~01m 10s
|++++++++++++++++++++++++++++++++ | 62% ~01m 08s
|++++++++++++++++++++++++++++++++ | 63% ~01m 07s
|+++++++++++++++++++++++++++++++++ | 64% ~01m 05s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 03s
|++++++++++++++++++++++++++++++++++ | 66% ~01m 01s
|++++++++++++++++++++++++++++++++++ | 67% ~59s
|+++++++++++++++++++++++++++++++++++ | 68% ~57s
|+++++++++++++++++++++++++++++++++++ | 69% ~55s
|++++++++++++++++++++++++++++++++++++ | 71% ~53s
|++++++++++++++++++++++++++++++++++++ | 72% ~51s
|+++++++++++++++++++++++++++++++++++++ | 73% ~49s
|+++++++++++++++++++++++++++++++++++++ | 74% ~47s
|++++++++++++++++++++++++++++++++++++++ | 75% ~46s
|++++++++++++++++++++++++++++++++++++++ | 76% ~44s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~42s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~40s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~38s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~36s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~34s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~32s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~30s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~28s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~27s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~25s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~21s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~19s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 00s
save(all_data.markers, cis_markers, cocl2_markers, dabtram_markers, file = 'all_data_markers.RData')
#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))
filtered_meta <- rep(0, length(names(all_data$Lineage)))
#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'
#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'
# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'
#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'
print(table(filtered_meta))
filtered_meta
Filtered out Large Resistant to Cis Large Resistant to CistoCis Large Resistant to CistoCoCl2
3337 1375 951 2078
Large Resistant to CistoDabTram Large Resistant to CoCl2 Large Resistant to CoCl2toCis Large Resistant to CoCl2toCoCl2
1394 1784 3010 11578
Large Resistant to CoCl2toDabTram Large Resistant to DabTram Large Resistant to DabTramtoCis Large Resistant to DabTramtoCoCl2
663 478 4234 2840
Large Resistant to DabTramtoDabTram No Barcode Resistant to Cis Resistant to CistoCis
3176 35314 331 67
Resistant to CistoCoCl2 Resistant to CistoDabTram Resistant to CoCl2 Resistant to CoCl2toCis
135 113 278 157
Resistant to CoCl2toCoCl2 Resistant to CoCl2toDabTram Resistant to DabTram Resistant to DabTramtoCis
55 93 337 225
Resistant to DabTramtoCoCl2 Resistant to DabTramtoDabTram
100 222
all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filtered
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = "Lineage") + theme(legend.position = 'none')
dev.off()
null device
1
#Looking into the one lineage that switches ngfr -> egfr
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_violin.pdf', height = 10, width = 30)
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning Inf
VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
dev.off()
null device
1
#VlnPlot(all_data, features = 'NGFR', idents = all_data$OG_condition == 'dabtram', group.by = "lineage")
#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2
switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])
DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))
switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)
DimPlot(dabtram, cells.highlight = list(switch_lin))
#Assign cluster assignments per lineage, find average score per lineage - make plots in order
dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
Calculating cluster 0
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Calculating cluster 1
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DimPlot(dabtram_both_times)
DimPlot(dabtram_both_times, group.by = 'OG_condition')
FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))
#average cell assignments per lineage in dabtram_maintained
#want to do a stacked barplot here-- copy paste code from condition clustering
#get lineage and cluster data from seurat object, switch cluster identifiers from 0,1 to -1,1 (egfr, ngfr)
clusters_per_lin <- data.frame (Cluster = as.numeric(as.character(dabtram_both_times$seurat_clusters)), Lineage = dabtram_both_times$Lineage, condition = dabtram_both_times$OG_condition)
clusters_per_lin$Cluster[clusters_per_lin$Cluster == 0] <- -1
clusters_per_lin_list <- list()
# Need to get percent values of EGFR for the lineage after the first treatment
for (i in fivecell_cDNA$DabTram){
currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')
Var1 <- c(-1,1)
Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == -1), # EGFR
sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == 1))
clusters_per_lin_list[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
clusters_per_lin_list[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list[[i]]$Var1)), clusters_per_lin_list[[i]]$Freq)
}
# Build a dataframe for plotting based on % EGFR or NGFR per lineage after first treatment
clusters_per_lin_df <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score')
for (i in names(clusters_per_lin_list)){
clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}
# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df <- clusters_per_lin_df[with(clusters_per_lin_df, order(-Score,Num_cells )),]
clusters_per_lin_df$Lineage <- factor(clusters_per_lin_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# make list object for after second treatment
clusters_per_lin_list_sec <- list()
# Need to get percent values of EGFR for the lineage after the second treatment
for (i in fivecell_cDNA$DabTram){
currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')
Var1 <- c(-1,1)
Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == -1), # EGFR
sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == 1))
clusters_per_lin_list_sec[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
clusters_per_lin_list_sec[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list_sec[[i]]$Var1)), clusters_per_lin_list_sec[[i]]$Freq)
}
# Build a dataframe for plotting based on % EGFR or NGFR per lineage after second treatment
clusters_per_lin_df_sec <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df_sec) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score', 'Cluster')
for (i in names(clusters_per_lin_list)){
if (sum(clusters_per_lin_list_sec[[i]]$Freq) < 5){
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 1, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'Died')) # Add Lineage died row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'EGFR')) # Add EGFR row
}else{ # Actually calculate percentages since the lineage survived/is more than 5 cells
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'Died')) # Add Lineage died row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}
}
# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df_sec$Lineage <- factor(clusters_per_lin_df_sec$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# Plot
p1 <- ggplot(clusters_per_lin_df, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#000000","#FFFFFF")) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2 <- ggplot(clusters_per_lin_df_sec, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#FF0000","#000000", '#FFFFFF')) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()
null device
1
# Get the number of cells in the DabTram resistant lineages in the conditions that got DabTram first
dabtramtococl2_dt_resist_lins <- list()
dabtramtocis_dt_resist_lins <- list()
for (i in fivecell_cDNA$DabTram){
dabtramtococl2_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtococl2']
dabtramtocis_dt_resist_lins[[i]] <- all_data$Lineage[all_data$Lineage == i & all_data$OG_condition == 'dabtramtocis']
}
# convert list to dataframe
dabtramtococl2_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtococl2_dt_resist_lins))
dabtramtococl2_dt_resist_lins_df$Lineage = rownames(dabtramtococl2_dt_resist_lins_df)
dabtramtococl2_dt_resist_lins_df$Lineage <- factor(dabtramtococl2_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
dabtramtocis_dt_resist_lins_df <- data.frame(num_cells = lengths(dabtramtocis_dt_resist_lins))
dabtramtocis_dt_resist_lins_df$Lineage = rownames(dabtramtocis_dt_resist_lins_df)
dabtramtocis_dt_resist_lins_df$Lineage <- factor(dabtramtocis_dt_resist_lins_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# Plot
p3 <- ggplot(dabtramtococl2_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#A2248E') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to CoCl2')
p4 <- ggplot(dabtramtocis_dt_resist_lins_df, aes(y = log(num_cells+1), x = Lineage)) + geom_col(fill = '#9D85BE') + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = 'DabTram to Cis')
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died_w_other_second_drugs.pdf', height = 14)
ggarrange(p1+ theme(legend.position = 'bottom'),p2+ theme(legend.position = 'bottom'),p3,p4, nrow = 4)
dev.off()
null device
1
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtram <- mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtram <- (mean_dabtram-mean(means_dabtram_sim))/sd(means_dabtram_sim)
pval_mean_dabtram <- pnorm(z_mean_dabtram, mean(means_dabtram_sim), sd(means_dabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtram <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtram <- (weighted_mean_dabtram-mean(weighted_means_dabtram_sim))/sd(weighted_means_dabtram_sim)
pval_wmean_dabtram <- pnorm(z_wmean_dabtram, mean(weighted_means_dabtram_sim), sd(weighted_means_dabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtram <- c()
for (i in 1:length(means_dabtram_sim)){
sim_maxes <- unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtram <- cbind(ttest_pval_dabtram,t.test(x = unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtram_lin_clust_list, dabtram_lin_clust_rand_list,z_mean_dabtram,pval_mean_dabtram, z_wmean_dabtram, ttest_pval_dabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
rm(dabtram)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtodabtram
dabtramtodabtram <- subset(all_data, idents = 'dabtramtodabtram') # Subset down to the dabtramtodabtram object
ElbowPlot(dabtramtodabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtodabtram <- FindNeighbors(dabtramtodabtram, dims = 1:10)
dabtramtodabtram <- FindClusters(dabtramtodabtram, resolution = 0.5)
dabtramtodabtram <- RunUMAP(dabtramtodabtram, dims = 1:10)
DimPlot(dabtramtodabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtodabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoDabTram){
temp_df <- as.data.frame(table(Idents(dabtramtodabtram)[dabtramtodabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtodabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtodabtram_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
dabtramtodabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoDabTram){
num_cells <- length(dabtramtodabtram$Lineage[dabtramtodabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtodabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtodabtram <- mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtodabtram <- (mean_dabtramtodabtram-mean(means_dabtramtodabtram_sim))/sd(means_dabtramtodabtram_sim)
pval_mean_dabtramtodabtram <- pnorm(z_mean_dabtramtodabtram, mean(means_dabtramtodabtram_sim), sd(means_dabtramtodabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtodabtram <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtodabtram <- (weighted_mean_dabtramtodabtram-mean(weighted_means_dabtramtodabtram_sim))/sd(weighted_means_dabtramtodabtram_sim)
pval_wmean_dabtramtodabtram <- pnorm(z_wmean_dabtramtodabtram, mean(weighted_means_dabtramtodabtram_sim), sd(weighted_means_dabtramtodabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtodabtram <- c()
for (i in 1:length(means_dabtramtodabtram_sim)){
sim_maxes <- unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtodabtram <- cbind(ttest_pval_dabtramtodabtram,t.test(x = unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtodabtram_lin_clust_list, dabtramtodabtram_lin_clust_rand_list,z_mean_dabtramtodabtram,pval_mean_dabtramtodabtram, z_wmean_dabtramtodabtram, ttest_pval_dabtramtodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
rm(dabtramtodabtram)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtocis
dabtramtocis <- subset(all_data, idents = 'dabtramtocis') # Subset down to the dabtramtocis object
ElbowPlot(dabtramtocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtocis <- FindNeighbors(dabtramtocis, dims = 1:10)
dabtramtocis <- FindClusters(dabtramtocis, resolution = 0.5)
dabtramtocis <- RunUMAP(dabtramtocis, dims = 1:10)
DimPlot(dabtramtocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtocis_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoCis){
temp_df <- as.data.frame(table(Idents(dabtramtocis)[dabtramtocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtocis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
dabtramtocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoCis){
num_cells <- length(dabtramtocis$Lineage[dabtramtocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtocis <- mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtocis <- (mean_dabtramtocis-mean(means_dabtramtocis_sim))/sd(means_dabtramtocis_sim)
pval_mean_dabtramtocis <- pnorm(z_mean_dabtramtocis, mean(means_dabtramtocis_sim), sd(means_dabtramtocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtocis <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtocis <- (weighted_mean_dabtramtocis-mean(weighted_means_dabtramtocis_sim))/sd(weighted_means_dabtramtocis_sim)
pval_wmean_dabtramtocis <- pnorm(z_wmean_dabtramtocis, mean(weighted_means_dabtramtocis_sim), sd(weighted_means_dabtramtocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtocis <- c()
for (i in 1:length(means_dabtramtocis_sim)){
sim_maxes <- unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtocis <- cbind(ttest_pval_dabtramtocis,t.test(x = unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtocis_lin_clust_list, dabtramtocis_lin_clust_rand_list,z_mean_dabtramtocis,pval_mean_dabtramtocis, z_wmean_dabtramtocis, ttest_pval_dabtramtocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
rm(dabtramtocis)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtococl2
dabtramtococl2 <- subset(all_data, idents = 'dabtramtococl2') # Subset down to the dabtramtococl2 object
ElbowPlot(dabtramtococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtococl2 <- FindNeighbors(dabtramtococl2, dims = 1:10)
dabtramtococl2 <- FindClusters(dabtramtococl2, resolution = 0.5)
dabtramtococl2 <- RunUMAP(dabtramtococl2, dims = 1:10)
DimPlot(dabtramtococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoCoCl2){
temp_df <- as.data.frame(table(Idents(dabtramtococl2)[dabtramtococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtococl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
dabtramtococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoCoCl2){
num_cells <- length(dabtramtococl2$Lineage[dabtramtococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtococl2 <- mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtococl2 <- (mean_dabtramtococl2-mean(means_dabtramtococl2_sim))/sd(means_dabtramtococl2_sim)
pval_mean_dabtramtococl2 <- pnorm(z_mean_dabtramtococl2, mean(means_dabtramtococl2_sim), sd(means_dabtramtococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtococl2 <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtococl2 <- (weighted_mean_dabtramtococl2-mean(weighted_means_dabtramtococl2_sim))/sd(weighted_means_dabtramtococl2_sim)
pval_wmean_dabtramtococl2 <- pnorm(z_wmean_dabtramtococl2, mean(weighted_means_dabtramtococl2_sim), sd(weighted_means_dabtramtococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtococl2 <- c()
for (i in 1:length(means_dabtramtococl2_sim)){
sim_maxes <- unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtococl2 <- cbind(ttest_pval_dabtramtococl2,t.test(x = unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtococl2_lin_clust_list, dabtramtococl2_lin_clust_rand_list,z_mean_dabtramtococl2,pval_mean_dabtramtococl2, z_wmean_dabtramtococl2, ttest_pval_dabtramtococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
rm(dabtramtococl2)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2
cocl2 <- subset(all_data, idents = 'cocl2') # Subset down to the cocl2 object
ElbowPlot(cocl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2 <- FindNeighbors(cocl2, dims = 1:10)
cocl2 <- FindClusters(cocl2, resolution = 0.5)
cocl2 <- RunUMAP(cocl2, dims = 1:10)
DimPlot(cocl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2){
temp_df <- as.data.frame(table(Idents(cocl2)[cocl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2){
num_cells <- length(cocl2$Lineage[cocl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2 <- mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2 <- (mean_cocl2-mean(means_cocl2_sim))/sd(means_cocl2_sim)
pval_mean_cocl2 <- pnorm(z_mean_cocl2, mean(means_cocl2_sim), sd(means_cocl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2 <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2 <- (weighted_mean_cocl2-mean(weighted_means_cocl2_sim))/sd(weighted_means_cocl2_sim)
pval_wmean_cocl2 <- pnorm(z_wmean_cocl2, mean(weighted_means_cocl2_sim), sd(weighted_means_cocl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2 <- c()
for (i in 1:length(means_cocl2_sim)){
sim_maxes <- unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2 <- cbind(ttest_pval_cocl2,t.test(x = unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2_lin_clust_list, cocl2_lin_clust_rand_list,z_mean_cocl2,pval_mean_cocl2, z_wmean_cocl2, ttest_pval_cocl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
rm(cocl2)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tococl2
cocl2tococl2 <- subset(all_data, idents = 'cocl2tococl2') # Subset down to the cocl2tococl2 object
ElbowPlot(cocl2tococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2tococl2 <- FindNeighbors(cocl2tococl2, dims = 1:10)
cocl2tococl2 <- FindClusters(cocl2tococl2, resolution = 0.5)
cocl2tococl2 <- RunUMAP(cocl2tococl2, dims = 1:10)
DimPlot(cocl2tococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2tococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toCoCl2){
temp_df <- as.data.frame(table(Idents(cocl2tococl2)[cocl2tococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2tococl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2tococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toCoCl2){
num_cells <- length(cocl2tococl2$Lineage[cocl2tococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2tococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tococl2 <- mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tococl2 <- (mean_cocl2tococl2-mean(means_cocl2tococl2_sim))/sd(means_cocl2tococl2_sim)
pval_mean_cocl2tococl2 <- pnorm(z_mean_cocl2tococl2, mean(means_cocl2tococl2_sim), sd(means_cocl2tococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tococl2 <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tococl2 <- (weighted_mean_cocl2tococl2-mean(weighted_means_cocl2tococl2_sim))/sd(weighted_means_cocl2tococl2_sim)
pval_wmean_cocl2tococl2 <- pnorm(z_wmean_cocl2tococl2, mean(weighted_means_cocl2tococl2_sim), sd(weighted_means_cocl2tococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tococl2 <- c()
for (i in 1:length(means_cocl2tococl2_sim)){
sim_maxes <- unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2tococl2 <- cbind(ttest_pval_cocl2tococl2,t.test(x = unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2tococl2_lin_clust_list, cocl2tococl2_lin_clust_rand_list,z_mean_cocl2tococl2,pval_mean_cocl2tococl2, z_wmean_cocl2tococl2, ttest_pval_cocl2tococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
rm(cocl2tococl2)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tocis
cocl2tocis <- subset(all_data, idents = 'cocl2tocis') # Subset down to the cocl2tocis object
ElbowPlot(cocl2tocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2tocis <- FindNeighbors(cocl2tocis, dims = 1:10)
cocl2tocis <- FindClusters(cocl2tocis, resolution = 0.5)
cocl2tocis <- RunUMAP(cocl2tocis, dims = 1:10)
DimPlot(cocl2tocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2tocis_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toCis){
temp_df <- as.data.frame(table(Idents(cocl2tocis)[cocl2tocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2tocis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2tocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toCis){
num_cells <- length(cocl2tocis$Lineage[cocl2tocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2tocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tocis <- mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tocis <- (mean_cocl2tocis-mean(means_cocl2tocis_sim))/sd(means_cocl2tocis_sim)
pval_mean_cocl2tocis <- pnorm(z_mean_cocl2tocis, mean(means_cocl2tocis_sim), sd(means_cocl2tocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tocis <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tocis <- (weighted_mean_cocl2tocis-mean(weighted_means_cocl2tocis_sim))/sd(weighted_means_cocl2tocis_sim)
pval_wmean_cocl2tocis <- pnorm(z_wmean_cocl2tocis, mean(weighted_means_cocl2tocis_sim), sd(weighted_means_cocl2tocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tocis <- c()
for (i in 1:length(means_cocl2tocis_sim)){
sim_maxes <- unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2tocis <- cbind(ttest_pval_cocl2tocis,t.test(x = unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2tocis_lin_clust_list, cocl2tocis_lin_clust_rand_list,z_mean_cocl2tocis,pval_mean_cocl2tocis, z_wmean_cocl2tocis, ttest_pval_cocl2tocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
rm(cocl2tocis)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2todabtram
cocl2todabtram <- subset(all_data, idents = 'cocl2todabtram') # Subset down to the cocl2todabtram object
ElbowPlot(cocl2todabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2todabtram <- FindNeighbors(cocl2todabtram, dims = 1:10)
cocl2todabtram <- FindClusters(cocl2todabtram, resolution = 0.5)
cocl2todabtram <- RunUMAP(cocl2todabtram, dims = 1:10)
DimPlot(cocl2todabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2todabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toDabTram){
temp_df <- as.data.frame(table(Idents(cocl2todabtram)[cocl2todabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2todabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2todabtram_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cocl2todabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toDabTram){
num_cells <- length(cocl2todabtram$Lineage[cocl2todabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2todabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2todabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2todabtram <- mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2todabtram <- (mean_cocl2todabtram-mean(means_cocl2todabtram_sim))/sd(means_cocl2todabtram_sim)
pval_mean_cocl2todabtram <- pnorm(z_mean_cocl2todabtram, mean(means_cocl2todabtram_sim), sd(means_cocl2todabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2todabtram <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2todabtram <- (weighted_mean_cocl2todabtram-mean(weighted_means_cocl2todabtram_sim))/sd(weighted_means_cocl2todabtram_sim)
pval_wmean_cocl2todabtram <- pnorm(z_wmean_cocl2todabtram, mean(weighted_means_cocl2todabtram_sim), sd(weighted_means_cocl2todabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2todabtram <- c()
for (i in 1:length(means_cocl2todabtram_sim)){
sim_maxes <- unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2todabtram <- cbind(ttest_pval_cocl2todabtram,t.test(x = unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2todabtram_lin_clust_list, cocl2todabtram_lin_clust_rand_list,z_mean_cocl2todabtram,pval_mean_cocl2todabtram, z_wmean_cocl2todabtram, ttest_pval_cocl2todabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
rm(cocl2todabtram)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cis
cis <- subset(all_data, idents = 'cis') # Subset down to the cis object
ElbowPlot(cis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cis <- FindNeighbors(cis, dims = 1:10)
cis <- FindClusters(cis, resolution = 0.5)
cis <- RunUMAP(cis, dims = 1:10)
DimPlot(cis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cis_lin_clust_list <- list()
for (i in fivecell_cDNA$Cis){
temp_df <- as.data.frame(table(Idents(cis)[cis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$Cis){
num_cells <- length(cis$Lineage[cis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cis <- mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cis <- (mean_cis-mean(means_cis_sim))/sd(means_cis_sim)
pval_mean_cis <- pnorm(z_mean_cis, mean(means_cis_sim), sd(means_cis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cis <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cis <- (weighted_mean_cis-mean(weighted_means_cis_sim))/sd(weighted_means_cis_sim)
pval_wmean_cis <- pnorm(z_wmean_cis, mean(weighted_means_cis_sim), sd(weighted_means_cis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cis <- c()
for (i in 1:length(means_cis_sim)){
sim_maxes <- unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cis <- cbind(ttest_pval_cis,t.test(x = unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cis_lin_clust_list, cis_lin_clust_rand_list,z_mean_cis,pval_mean_cis, z_wmean_cis, ttest_pval_cis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
rm(cis)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistocis
cistocis <- subset(all_data, idents = 'cistocis') # Subset down to the cistocis object
ElbowPlot(cistocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistocis <- FindNeighbors(cistocis, dims = 1:10)
cistocis <- FindClusters(cistocis, resolution = 0.5)
cistocis <- RunUMAP(cistocis, dims = 1:10)
DimPlot(cistocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistocis_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoCis){
temp_df <- as.data.frame(table(Idents(cistocis)[cistocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistocis_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cistocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoCis){
num_cells <- length(cistocis$Lineage[cistocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistocis <- mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistocis <- (mean_cistocis-mean(means_cistocis_sim))/sd(means_cistocis_sim)
pval_mean_cistocis <- pnorm(z_mean_cistocis, mean(means_cistocis_sim), sd(means_cistocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistocis <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistocis <- (weighted_mean_cistocis-mean(weighted_means_cistocis_sim))/sd(weighted_means_cistocis_sim)
pval_wmean_cistocis <- pnorm(z_wmean_cistocis, mean(weighted_means_cistocis_sim), sd(weighted_means_cistocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistocis <- c()
for (i in 1:length(means_cistocis_sim)){
sim_maxes <- unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistocis <- cbind(ttest_pval_cistocis,t.test(x = unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistocis_lin_clust_list, cistocis_lin_clust_rand_list,z_mean_cistocis,pval_mean_cistocis, z_wmean_cistocis, ttest_pval_cistocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
rm(cistocis)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistodabtram
cistodabtram <- subset(all_data, idents = 'cistodabtram') # Subset down to the cistodabtram object
ElbowPlot(cistodabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistodabtram <- FindNeighbors(cistodabtram, dims = 1:10)
cistodabtram <- FindClusters(cistodabtram, resolution = 0.5)
cistodabtram <- RunUMAP(cistodabtram, dims = 1:10)
DimPlot(cistodabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistodabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoDabTram){
temp_df <- as.data.frame(table(Idents(cistodabtram)[cistodabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistodabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistodabtram_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cistodabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoDabTram){
num_cells <- length(cistodabtram$Lineage[cistodabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistodabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistodabtram <- mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistodabtram <- (mean_cistodabtram-mean(means_cistodabtram_sim))/sd(means_cistodabtram_sim)
pval_mean_cistodabtram <- pnorm(z_mean_cistodabtram, mean(means_cistodabtram_sim), sd(means_cistodabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistodabtram <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistodabtram <- (weighted_mean_cistodabtram-mean(weighted_means_cistodabtram_sim))/sd(weighted_means_cistodabtram_sim)
pval_wmean_cistodabtram <- pnorm(z_wmean_cistodabtram, mean(weighted_means_cistodabtram_sim), sd(weighted_means_cistodabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistodabtram <- c()
for (i in 1:length(means_cistodabtram_sim)){
sim_maxes <- unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistodabtram <- cbind(ttest_pval_cistodabtram,t.test(x = unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistodabtram_lin_clust_list, cistodabtram_lin_clust_rand_list,z_mean_cistodabtram,pval_mean_cistodabtram, z_wmean_cistodabtram, ttest_pval_cistodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
rm(cistodabtram)
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistococl2
cistococl2 <- subset(all_data, idents = 'cistococl2') # Subset down to the cistococl2 object
ElbowPlot(cistococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistococl2 <- FindNeighbors(cistococl2, dims = 1:10)
cistococl2 <- FindClusters(cistococl2, resolution = 0.5)
cistococl2 <- RunUMAP(cistococl2, dims = 1:10)
DimPlot(cistococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoCoCl2){
temp_df <- as.data.frame(table(Idents(cistococl2)[cistococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistococl2_lin_clust_rand_list <- list()
num_iter <- 800
for(j in 1:num_iter){
set.seed(j)
cistococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoCoCl2){
num_cells <- length(cistococl2$Lineage[cistococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistococl2 <- mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistococl2 <- (mean_cistococl2-mean(means_cistococl2_sim))/sd(means_cistococl2_sim)
pval_mean_cistococl2 <- pnorm(z_mean_cistococl2, mean(means_cistococl2_sim), sd(means_cistococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistococl2 <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistococl2 <- (weighted_mean_cistococl2-mean(weighted_means_cistococl2_sim))/sd(weighted_means_cistococl2_sim)
pval_wmean_cistococl2 <- pnorm(z_wmean_cistococl2, mean(weighted_means_cistococl2_sim), sd(weighted_means_cistococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistococl2 <- c()
for (i in 1:length(means_cistococl2_sim)){
sim_maxes <- unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistococl2 <- cbind(ttest_pval_cistococl2,t.test(x = unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistococl2_lin_clust_list, cistococl2_lin_clust_rand_list,z_mean_cistococl2,pval_mean_cistococl2, z_wmean_cistococl2, ttest_pval_cistococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
rm(cistococl2)
sim_results <- rbind(
dabtram <- data.frame("Mean_Zscore" = z_mean_dabtram, "Mean_pval" = pval_mean_dabtram, "Weighted_Mean_Zscore" = z_wmean_dabtram, "Weighted_Mean_pval" = pval_wmean_dabtram, ttest_sims_sig = sum(ttest_pval_dabtram <= 0.05)),
dabtramtodabtram <- data.frame("Mean_Zscore" = z_mean_dabtramtodabtram, "Mean_pval" = pval_mean_dabtramtodabtram, "Weighted_Mean_Zscore" = z_wmean_dabtramtodabtram, "Weighted_Mean_pval" = pval_wmean_dabtramtodabtram, ttest_sims_sig = sum(ttest_pval_dabtramtodabtram <= 0.05)),
dabtramtococl2 <- data.frame("Mean_Zscore" = z_mean_dabtramtococl2, "Mean_pval" = pval_mean_dabtramtococl2, "Weighted_Mean_Zscore" = z_wmean_dabtramtococl2, "Weighted_Mean_pval" = pval_wmean_dabtramtococl2, ttest_sims_sig = sum(ttest_pval_dabtramtococl2 <= 0.05)),
dabtramtocis <- data.frame("Mean_Zscore" = z_mean_dabtramtocis, "Mean_pval" = pval_mean_dabtramtocis, "Weighted_Mean_Zscore" = z_wmean_dabtramtocis, "Weighted_Mean_pval" = pval_wmean_dabtramtocis, ttest_sims_sig = sum(ttest_pval_dabtramtocis <= 0.05)),
cis <- data.frame("Mean_Zscore" = z_mean_cis, "Mean_pval" = pval_mean_cis, "Weighted_Mean_Zscore" = z_wmean_cis, "Weighted_Mean_pval" = pval_wmean_cis, ttest_sims_sig = sum(ttest_pval_cis <= 0.05)),
cistodabtram <- data.frame("Mean_Zscore" = z_mean_cistodabtram, "Mean_pval" = pval_mean_cistodabtram, "Weighted_Mean_Zscore" = z_wmean_cistodabtram, "Weighted_Mean_pval" = pval_wmean_cistodabtram, ttest_sims_sig = sum(ttest_pval_cistodabtram <= 0.05)),
cistococl2 <- data.frame("Mean_Zscore" = z_mean_cistococl2, "Mean_pval" = pval_mean_cistococl2, "Weighted_Mean_Zscore" = z_wmean_cistococl2, "Weighted_Mean_pval" = pval_wmean_cistococl2, ttest_sims_sig = sum(ttest_pval_cistococl2 <= 0.05)),
cistocis <- data.frame("Mean_Zscore" = z_mean_cistocis, "Mean_pval" = pval_mean_cistocis, "Weighted_Mean_Zscore" = z_wmean_cistocis, "Weighted_Mean_pval" = pval_wmean_cistocis, ttest_sims_sig = sum(ttest_pval_cistocis <= 0.05)),
cocl2 <- data.frame("Mean_Zscore" = z_mean_cocl2, "Mean_pval" = pval_mean_cocl2, "Weighted_Mean_Zscore" = z_wmean_cocl2, "Weighted_Mean_pval" = pval_wmean_cocl2, ttest_sims_sig = sum(ttest_pval_cocl2 <= 0.05)),
cocl2todabtram <- data.frame("Mean_Zscore" = z_mean_cocl2todabtram, "Mean_pval" = pval_mean_cocl2todabtram, "Weighted_Mean_Zscore" = z_wmean_cocl2todabtram, "Weighted_Mean_pval" = pval_wmean_cocl2todabtram, ttest_sims_sig = sum(ttest_pval_cocl2todabtram <= 0.05)),
cocl2tococl2 <- data.frame("Mean_Zscore" = z_mean_cocl2tococl2, "Mean_pval" = pval_mean_cocl2tococl2, "Weighted_Mean_Zscore" = z_wmean_cocl2tococl2, "Weighted_Mean_pval" = pval_wmean_cocl2tococl2, ttest_sims_sig = sum(ttest_pval_cocl2tococl2 <= 0.05)),
cocl2tocis <- data.frame("Mean_Zscore" = z_mean_cocl2tocis, "Mean_pval" = pval_mean_cocl2tocis, "Weighted_Mean_Zscore" = z_wmean_cocl2tocis, "Weighted_Mean_pval" = pval_wmean_cocl2tocis, ttest_sims_sig = sum(ttest_pval_cocl2tocis <= 0.05))
)
rownames(sim_results) <- c("dabtram", "dabtramtodabtram", "dabtramtococl2", "dabtramtocis", "cis", "cistodabtram", "cistococl2", "cistocis", "cocl2", "cocl2todabtram", "cocl2tococl2", "cocl2tocis")
write.csv(sim_results, '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv', row.names = T)
sim_results <- read.csv('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
breaks <- seq(0, 1, 0.1)
# DabTram
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/proportion_clusters_heatmaps.pdf', width = 16)
dabtram_pcnts <- as.data.frame(sapply(dabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram", breaks = breaks) #
p
dabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtram_lin_clust_rand_list))
rownames(dabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtram_pcnts_rand <- dabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram Rand", breaks = breaks, cluster_cols = F)
# DabTram to DabTram
dabtramtodabtram_pcnts <- as.data.frame(sapply(dabtramtodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtodabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram", breaks = breaks)
p
dabtramtodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtodabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtodabtram_lin_clust_rand_list))
rownames(dabtramtodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtodabtram_pcnts_rand <- dabtramtodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to DabTram Rand", breaks = breaks, cluster_cols = F)
# DabTram to CoCl2
dabtramtococl2_pcnts <- as.data.frame(sapply(dabtramtococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtococl2_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtococl2_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2", breaks = breaks)
p
dabtramtococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtococl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtococl2_lin_clust_rand_list))
rownames(dabtramtococl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(dabtramtococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtococl2_pcnts_rand <- dabtramtococl2_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to CoCl2 Rand", breaks = breaks, cluster_cols = F)
# DabTram to Cis
dabtramtocis_pcnts <- as.data.frame(sapply(dabtramtocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(dabtramtocis_pcnts) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(dabtramtocis_pcnts,cluster_rows = F, color = inferno(11), main = "DabTram to Cis", breaks = breaks)
p
dabtramtocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(dabtramtocis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(dabtramtocis_lin_clust_rand_list))
rownames(dabtramtocis_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(dabtramtocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
dabtramtocis_pcnts_rand <- dabtramtocis_pcnts_rand[,p$tree_col$order]
pheatmap(dabtramtocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "DabTram to Cis Rand", breaks = breaks, cluster_cols = F)
# CoCl2 objects
cocl2_pcnts <- as.data.frame(sapply(cocl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2", breaks = breaks)
p
cocl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2_lin_clust_rand_list))
rownames(cocl2_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cocl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2_pcnts_rand <- cocl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 Rand", breaks = breaks, cluster_cols = F)
# CoCl2 to DabTram
cocl2todabtram_pcnts <- as.data.frame(sapply(cocl2todabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2todabtram_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2todabtram_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram", breaks = breaks)
p
cocl2todabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2todabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2todabtram_lin_clust_rand_list))
rownames(cocl2todabtram_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cocl2todabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2todabtram_pcnts_rand <- cocl2todabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2todabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to DabTram Rand", breaks = breaks, cluster_cols = F)
# CoCl2 to CoCl2
cocl2tococl2_pcnts <- as.data.frame(sapply(cocl2tococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tococl2_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tococl2_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2", breaks = breaks)
p
cocl2tococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tococl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tococl2_lin_clust_rand_list))
rownames(cocl2tococl2_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tococl2_pcnts_rand <- cocl2tococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to CoCl2 Rand", breaks = breaks, cluster_cols = F)
# CoCl2 to Cis
cocl2tocis_pcnts <- as.data.frame(sapply(cocl2tocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cocl2tocis_pcnts) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cocl2tocis_pcnts,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis", breaks = breaks)
p
cocl2tocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cocl2tocis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cocl2tocis_lin_clust_rand_list))
rownames(cocl2tocis_pcnts_rand) <- c("0","1","2","3","4","5")
colnames(cocl2tocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cocl2tocis_pcnts_rand <- cocl2tocis_pcnts_rand[,p$tree_col$order]
pheatmap(cocl2tocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "CoCl2 to Cis Rand", breaks = breaks, cluster_cols = F)
# Cis
cis_pcnts <- as.data.frame(sapply(cis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cis_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cis_pcnts,cluster_rows = F, color = inferno(10), main = "Cis", breaks = breaks)
p
cis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cis_lin_clust_rand_list))
rownames(cis_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cis_pcnts_rand <- cis_pcnts_rand[,p$tree_col$order]
pheatmap(cis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis Rand", breaks = breaks, cluster_cols = F)
# Cis to DabTram
cistodabtram_pcnts <- as.data.frame(sapply(cistodabtram_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistodabtram_pcnts) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistodabtram_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to DabTram", breaks = breaks)
p
cistodabtram_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistodabtram_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistodabtram_lin_clust_rand_list))
rownames(cistodabtram_pcnts_rand) <- c("0","1","2","3","4","5","6","7")
colnames(cistodabtram_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistodabtram_pcnts_rand <- cistodabtram_pcnts_rand[,p$tree_col$order]
pheatmap(cistodabtram_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to DabTram Rand", breaks = breaks, cluster_cols = F)
# Cis to CoCl2
cistococl2_pcnts <- as.data.frame(sapply(cistococl2_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistococl2_pcnts) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistococl2_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2", breaks = breaks)
p
cistococl2_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistococl2_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistococl2_lin_clust_rand_list))
rownames(cistococl2_pcnts_rand) <- c("0","1","2","3","4","5","6")
colnames(cistococl2_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistococl2_pcnts_rand <- cistococl2_pcnts_rand[,p$tree_col$order]
pheatmap(cistococl2_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to CoCl2 Rand", breaks = breaks, cluster_cols = F)
# Cis to Cis
cistocis_pcnts <- as.data.frame(sapply(cistocis_lin_clust_list, '[[', 3))
cell_nums <- sapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))
rownames(cistocis_pcnts) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
p <- pheatmap(cistocis_pcnts,cluster_rows = F, color = inferno(11), main = "Cis to Cis", breaks = breaks)
p
cistocis_pcnts_rand <- as.data.frame(Reduce(`+`, lapply(cistocis_lin_clust_rand_list, function(x) {
sapply(x, function(y) y$Pcnt_cells)
}))/ length(cistocis_lin_clust_rand_list))
rownames(cistocis_pcnts_rand) <- c("0","1","2","3","4","5","6","7","8")
colnames(cistocis_pcnts_rand) <- paste(names(cell_nums), rep('-',length(cell_nums)), cell_nums, rep('cells', length(cell_nums)))
cistocis_pcnts_rand <- cistocis_pcnts_rand[,p$tree_col$order]
pheatmap(cistocis_pcnts_rand,cluster_rows = F, color = inferno(11), main = "Cis to Cis Rand", breaks = breaks, cluster_cols = F)
dev.off()
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/weighted_mean_cluster_assignments_test_stats.pdf')
# DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#623594', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# DabTram to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#561e59', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# DabTram to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A2248E', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# DabTram to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#9D85BE', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'DabTram to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#0F8241', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2 to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#10413B', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2 to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6ABD45', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# CoCl2 to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#6DC49C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'CoCl2 to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis to DabTram
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#A23622', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to DabTram') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis to CoCl2
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#C96D29', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to CoCl2') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
# Cis to Cis
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
wmean <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
df <- data.frame(weighted_mean = sapply(1:length(cistocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells))))))
ggplot(df, aes(x = weighted_mean))+geom_histogram(fill = '#FBD08C', color = 'black', binwidth = 0.005) + geom_vline(xintercept = wmean, linetype = 'dashed', color = 'red') + geom_text(aes(x = wmean+.03, label = round(wmean, 3), y = 500), color = 'red') + labs(y = 'Number of simulations', x = 'Weighted mean of percent of cells in dominant lineage', title = 'Cis to Cis') + xlim(0.15, 0.65) + ylim(0,850) + geom_text(aes(x = wmean+.03, label = 'Observed', y = 525), color = 'red') + theme_classic()
dev.off()